Organizational Meeting for all
FALL term
courses whose times are not listed below:
10:00 AM, Tuesday, August 28, 2018.

Those interested in attending one of the courses but unable to be present at this
meeting should inform David Pollard
beforehand and submit their schedules.

Courses numbered 600 or above
(such as Stat 610a) are intended
primarily for graduate students. If such a course does
not have an undergraduate cross-listing,
undergraduates need special permission to enroll.

Instructor: David BrindaTime: Mon, Wed, Fri 10:30-11:20Place: TBA
An introduction to statistical reasoning. Topics include numerical and
graphical summaries of data, data acquisition and experimental design,
probability, hypothesis testing, confidence intervals, correlation and
regression. Application of statistical concepts to data; analysis of
real-world problems. A faster-paced version of this course with a higher
level of computing is being created: See STAT 220a.

Instructor: Jonathan Reuning-Scherer and StaffTime: Tues, Thurs 1:00-2:15 Place: OML 202Webpage: http://www.stat.yale.edu/Courses/QR/stat101106.html
A basic introduction to statistics, including numerical and graphical summaries
of data, probability, hypothesis testing, confidence intervals, and regression.
Each course focuses on applications to a particular field of study and is taught
jointly by two instructors, one specializing in statistics and the other in the
relevant area of application. The first seven weeks of classes are attended by
all students in STAT 101-106 together, as general concepts and methods of
statistics are developed. The remaining weeks are divided into field-specific
sections that develop the concepts with examples and applications. Computers are
used for data analysis. These courses are alternatives; they do not form a
sequence and only one may be taken for credit. No prerequisites beyond high
school algebra. May not be taken after STAT 100 or 109.

Students enrolled in STAT 101-106 who wish to change to STAT 109, or those
enrolled in STAT 109 who wish to change to STAT 101-106, must submit a course
change notice, signed by the instructor, to their residential college dean by
Friday, September 28. The approval of the Committee on Honors and Academic
Standing is not required.

Instructor: Jonathan Reuning-SchererTime: Tues, Thurs 1:00-2:15Place: OML 202
General concepts and methods in statistics. Meets for the first half of the term
only. May not be taken after STAT 100 or 101-106.

Instructor: Susan WangTime: Tues, Thurs 9:00-10:15Place: DL 220
Survey of statistical methods: plots, transformations, regression, analysis of
variance, clustering, principal components, contingency tables, and time series
analysis. The R computing language and Web data sources are used. After STAT 100 or the equivalent or with permission from the instructor; students without
prior coursework in statistics should take STAT 100, 10X, or 200.

Instructor: Jonathan Reuning-SchererTime: Tues, Thurs 9:00-10:15Place: TBA
Survey of statistical methods: plots, transformations, regression, analysis of
variance, clustering, principal components, contingency tables, and time series
analysis. The R computing language and Web data sources are used. After STAT
100 or the equivalent or with permission from the instructor; students from STAT 200 may be permitted in 230 but are encouraged to take 361 and/or 325.

Instructor: John Lafferty and Derek FengTime: Tues, Thurs 9:00-10:15Place: WLH 201
Techniques for data mining and machine learning are covered
from both a statistical and a computational perspective, including
support vector machines, bagging, boosting, neural networks, and other
nonlinear and nonparametric regression methods. The course will give
the basic ideas and intuition behind these methods, a more formal
understanding of how and why they work, and opportunities to experiment
with machine learning algorithms and apply them to data. After STAT
242b.

Instructor: Derek FengTime: Mon, Wed 11:35-12:50Place: SCL 160
Techniques for data mining and machine learning are covered
from both a statistical and a computational perspective, including
support vector machines, bagging, boosting, neural networks, and other
nonlinear and nonparametric regression methods. The course will give
the basic ideas and intuition behind these methods, a more formal
understanding of how and why they work, and opportunities to experiment
with machine learning algorithms and apply them to data. After STAT
242b.

Instructor: Susan WangTime: Tues, Thurs 11:35-12:50Place: TBAWebpage: https://classesv2.yale.edu/
Statistical analysis of a variety of statistical problems using real data. Emphasis on methods of choosing data, acquiring data, assessing data quality, and the issues posed by extremely large data sets. Extensive computations using R.
This is a senior seminar of limited size, but other students may join if space permits. A final project is required. S&DS or Applied Math majors who previously took Statistical Case Studies are not permitted to take this course.

Instructor: StaffTime: -Place: -
Directed individual study for qualified students who wish to investigate an area
of statistics not covered in regular courses. A student must be sponsored by a
faculty member who sets the requirements and meets regularly with the student.
Enrollment requires a written plan of study approved by the faculty adviser and
the director of undergraduate studies.

Instructor: Susan WangTime: Mon, Wed 1:00-2:15Place: WTS A74Webpage: https://classesv2.yale.edu/
Statistical analysis of a variety of statistical problems using real data. Emphasis on methods of choosing data, acquiring data, assessing data quality, and the issues posed by extremely large data sets. Extensive computations using R. Limited size, with permission from the instructor required.
STARRED? STAT 425 is a senior capstone version of this course that include a
final project. Can both be taken? Probably not.

Instructor: DGSTime: -Place: -
Individual one-semester projects, with students working on
studies outside the Department, under the guidance of a statistician.
This course is a one-credit requirement for the Ph.D. degree.

Instructor: Derek FengTime: Fri 2:30-4:30Place: 24 Hillhouse Rm 107Webpage: http://www.stat.yale.edu/~jay/627.html
Statistical consulting and collaborative research projects often require
statisticians to explore new topics outside their area of expertise. This course
exposes students to real problems, requiring them to draw on their expertise in
probability, statistics, and data analysis. Students complete the course with
individual projects supervised jointly by faculty outside the department and by
one of the instructors. Students enroll for both terms and receive one credit at
the end of the year.

Instructor: Hongyu ZhaoTime: Thur 1:00-2:50 pmPlace: TBD
Introduction to problems, algorithms, and data analysis approaches in
computational biology and bioinformatics; stochastic modeling and statistical
methods applied to problems such as mapping disease-associated genes, analyzing
gene expression microarray data, sequence alignment, and SNP analysis.
Statistical methods include maximum likelihood, EM, Bayesian inference, Markov
chain Monte Carlo, and some methods of classification and clustering; models
include hidden Markov models, Bayesian networks, and the coalescent. The
limitations of current models, and the future opportunities for model building,
are critically addressed. Prerequisite: STAT 661a, 538a, or 542b. Prior
knowledge of biology is not required, but some interest in the subject and a
willingness to carry out calculations using R is assumed.

Instructor: Roy LedermanTime: TBDPlace: TBD
The course explores the mechanics of the interface between
mathematics, computation and statistics in data analysis.
We will discuss topics in numerical computation, complexity,
programming and prototyping. Assignments will include theory,
programming, data analysis, individual work, collaborative work and making mistakes.

Prerequisites: Linear algebra and some experience with programming (any language).

Instructor: Tim GregoireTime: Tues, Thurs 10:30-11:50Place: TBD
An introduction to spatial statistical techniques with computer applications.
Topics include spatial sampling, visualizing spatial data, quantifying spatial
association and autocorrelation, interpolation methods, fitting variograms,
kriging, and related modeling techniques for spatially correlated data. Examples
are drawn from ecology, sociology, public health, and subjects proposed by
students. Four to five lab/homework assignments and a final project. The class
makes extensive use of the R programming language as well as
ArcGIS.

Instructor: SahandTime: Tues 2:30-5:00 pmPlace: 24 Hillhouse Rm 107
In this course we will review the recent advances in high-dimensional
statistics. We will cover concepts in empirical process theory,
concentration of measure, and random matrix theory in the context of
understanding the statistical properties of high-dimensional
estimation methods. In this discussion we will also overview the
computational constraints that are involved with solving
high-dimensional problems and touch upon concepts in convex
optimization and online learning.

Instructor: Andrew BarronTime: Mon, Wed 9:00-10:15Place: 24 Hillhouse Room 107
Modern developments of high-dimensional function estimation, building from classical one-dimensional ingredients. Theory and methods for approximation, estimation, and computation. The blessing and the curse of high-dimensionality. Piece-wise polynomial, sinusoidal, and sigmoidal (artificial neural network) models. Product and ridge-basis models. Selection criteria. Deterministic and stochastic optimization strategies, including gradient methods, greedy algorithms, annealing and the associated theory of evolution of the parameters of the function estimates. Students will be responsible for a literature-based theory project/presentation and a computational project/presentation.

Instructor: Sekhar Tatikonda and David PollardTime: Fri 11:00-1:00Place: 24 Hillhouse Rm 107Webpage: http://www.stat.yale.edu/~ypng
Continuation of the Yale Probability Group Seminar. Student
and faculty explanations of current research in areas such as random
graph theory, spectral graph theory, Markov chains on graphs, and the
objective method.